"Finding and Saving Image Assets for Mini Program Development and Design" matching MCP servers:

A simple server that acts as a Master Control Program (MCP) for unified interaction with OpenAI and Anthropic (Claude) AI models through a single API endpoint.

Enables AI assistants to perform high-performance 3D CAD design, ultra-fast multi-view rendering, immutable versioned project management, and geometry/mesh verification for 3D printing on Windows.

Facilitates spec-driven development workflows by providing structured prompts for generating requirements in EARS format, design documents, and implementation code following a systematic approach.

Enables Large Language Models to access and manipulate Autodesk Revit models through a pyRevit-based bridge and the Model Context Protocol.

Gives LLM agents spatial awareness inside Autodesk Maya, enabling scene inspection and measurement, aesthetic and engineering audits, camera planning, checkpoint/rollback safety, asset import, and viewport or render previews. Agents can also execute arbitrary Python in Maya to build and verify 3D scenes against engineering rules instead of writing blind.

Enables AI agents to invoke a local Stata installation, including a Windows Stata build under Wine on Linux and macOS, for regression analysis, econometrics, paper replication, and empirical research with command guards, resource monitoring, and automatic log capture.

A Model Context Protocol server for JavaScript/TypeScript development, providing tools to run scripts, tests, builds, and dependency management with framework and package manager detection.

A production-ready foundation template for building Model Context Protocol (MCP) servers with FastAPI, featuring modular tools, comprehensive testing, and OpenShift deployment configurations. Includes automated transformation scripts to create custom domain-specific MCP servers.

A comprehensive framework for building reliable Python MCP servers that enforces incremental development practices through Cursor Rules, featuring built-in security measures and a smart rule system.

Enable Claude (or any other LLM) to interactively debug your code (set breakpoints and evaluate expressions in stack frame). It's language-agnostic, assuming debugger console support and valid launch.json for debugging in VSCode.

Provides a Docker-based development environment with Python and Node.js MCP servers, enabling file operations, SQL queries, Redis caching, and debugging for building and testing MCP servers.

A Model Context Protocol server that enables Claude to manage software development projects with complete context awareness and code execution through Docker environments.

MCP server that gives AI agents isolated, named Linux computers with stable IDs and lifecycle control. Supports multiple backends like Docker, Fly Machines, Modal, and E2B, with a dashboard for managing sandboxes.

Enables AI-assisted development for Webasyst framework projects, including creating and managing apps, plugins, themes, and configurations through natural language commands.

The AIGC solution based on the MCP protocol seamlessly transforms the ComfyUI workflow into an MCP Tool with zero code, enabling LLMS and ComfyUI to join forces and focus on image generation tasks. qwen-image, flux, and FLUx-KREA can be used for image generation.

Enables AI coding tools to connect and authorize local game projects, then drive source edits, validation, preview, version saving, and mode-based creation workflows.

A FastMCP server implementation that provides a standardized interface for accessing AI models hosted on Replicate's API, currently supporting image generation with customizable parameters.

A starter template for creating MCP servers that work with Puch AI, featuring ready-to-use tools for job searching and image processing. Includes examples for Bearer token authentication, OAuth integration with Google and GitHub, and demonstrates user-scoped data management.

Enables AI assistants to operate directly inside a running Unity Editor project, using built-in tools for scene creation, script generation, play mode automation, input simulation, and performance analysis.

Enables AI clients to interact with and control the Unity Editor through a Python MCP server bridge, allowing natural language-based Unity project manipulation.